基于局部连续性与全局相似性的光谱保持型亚像元映射

遥感图像的像元级分类精度受混合像元的影响. 亚像元映射以像元分解获得的丰度值为基础,在地物分布规律的约束下,细化估计各类地物的亚像元级分布模式. 本文同时考虑了地物分布的空间与光谱信息,提出了一种基于局部连续性与全局相似性的光谱保持型亚像元映射算法. 针对地物的空间分布特性,提出了利用类内离散度对局部连续性进行建模,并通过相似分布像元表示误差引入全局相似性约束项. 针对地物的光谱特性,采用最小化光谱误差约束了亚像元映射过程中的光谱无失真性. 模拟数据与真实数据上的实验结果表明,本文算法比其他同类算法具有更高的估计精度,且更适合于实际应用.     

遥感影像亚像元定位研究综述

遥感影像亚像元定位是在混合像元分解基础上,利用地物空间分布特征确定不同地物类型在混合像元中的具体位置,得到亚像元尺度的地物分类图,是一种有效解决混合像元空间不确定性的方法。首先介绍遥感影像亚像元定位的基本概念,分析亚像元定位的理论模型和求解算法;然后总结亚像元定位模型的误差来源、精度评价方法以及结果不确定性的表达手段,同时讨论利用辅助数据源提高亚像元定位精度的主要方法;最后对亚像元定位的研究趋势做了进一步展望。     

遥感影像亚像元制图方法研究进展综述

遥感影像混合像元的普遍存在给遥感影像解译造成困扰。有效处理混合像元问题,细化分类结果,获得更为精细的地物细节信息就需要进行亚像元绘图。目前亚像元制图方法主要包括3个步骤:① 混合像元分解;② 提取软信息;③ 亚像元制图。总结归纳了近年来遥感影像亚像元绘图领域的研究进展和成果,详细阐述了亚像元制图的步骤及涉及的研究方法。依据辅助信息的类型将亚像元绘图方法大致划分为:基于空间相关性、基于空间结构信息、基于神经网络、基于像元交换途径的4类亚像元分类方法,并分别对各种方法的优缺点进行了分析对比。最后,评述了亚像元制图的发展趋势。     

Sub-pixel mapping of remotely sensed imagery with hybrid intra- and inter-pixel dependence

Sub-pixel mapping of remotely sensed imagery is often performed by assuming that land cover is spatially dependent both within and between image pixels. Intra- and inter-pixel dependencies are two widely used approaches to represent different land-cover spatial dependencies at present. However, merely using intra- or inter-pixel dependence alone often fails to fully describe land-cover spatial dependence, making current sub-pixel mapping models defective. A more reasonable object for sub-pixel mapping is maximizing both intra- and inter-pixel dependencies simultaneously instead of using only one of them. In this article, the differences between intra- and inter-pixel dependencies are discussed theoretically, and a novel sub-pixel mapping model aiming to maximize hybrid intra- and inter-pixel dependence is proposed. In the proposed model, spatial dependence is formulated as a weighted sum of intra-pixel dependence and inter-pixel dependence to satisfy both intra- and inter-pixel dependencies. By application to artificial and synthetic images, the proposed model was evaluated both visually and quantitatively by comparing with three representative sub-pixel mapping algorithms: the pixel swapping algorithm, the sub-pixel/pixel attraction algorithm, and the pixel swapping initialized with sub-pixel/pixel attraction algorithm. The results showed increased accuracy of the proposed algorithm when compared with these traditional sub-pixel mapping algorithms.     

Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation.Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties.     

Super-resolution mapping using Hopfield Neural Network with panchromatic imagery

Land-cover proportions of mixed pixels can be predicted using soft classification. From the land-cover proportions, a hard land-cover map can be predicted at sub-pixel spatial resolution using super-resolution mapping techniques. It has been demonstrated that the Hopfield Neural Network (HNN) provides a suitable method for super-resolution mapping. To increase the detail and accuracy of the sub-pixel land-cover map, supplementary information at an intermediate spatial resolution can be used. In this research, panchromatic (PAN) imagery was used as an additional source of information for super-resolution mapping. Information from the PAN image was captured by a new PAN reflectance constraint in the energy function of the HNN. The value of the new PAN reflectance constraint was defined based on forward and inverse models with local end-member spectra and local convolution weighting factors. Two sets of simulated and degraded data were used to test the new technique. The results indicate that PAN imagery can be used as a source of supplementary information to increase the detail and accuracy of sub-pixel land-cover maps produced by super-resolution mapping from land-cover proportion images.     

Spatially adaptive smoothing parameter selection for Markov random field based sub-pixel mapping of remotely sensed images

Sub-pixel mapping is a process to provide the spatial distributions of land cover classes with finer spatial resolution than the size of a remotely sensed image pixel. Traditional Markov random field-based sub-pixel mapping (MRF_SPM) adopts a fixed smoothing parameter estimated based on the entire image to balance the spatial and spectral energies. However, the spectra of the remotely sensed pixels are always spatially variable. Adopting a fixed smoothing parameter disregards the local properties provided by each pixel spectrum, and may probably lead to insufficient smoothing in the homogeneous region and over-smoothing between class boundaries simultaneously. This article proposes a spatially adaptive parameter selection method for the MRF_SPM model to overcome the limitation of the fixed parameter. As pixel class proportions are indicators of the type and proportion of land cover classes within each coarse pixel, in the proposed method, fraction images providing pixel class proportions as local properties of each pixel spectrum are employed to constrain the smoothing parameter. Consequently, the smoothing parameter is spatially adaptive to each pixel spectrum of the remotely sensed image. Synthetic images and IKONOS multi-spectral images were employed. Results showed that compared with the hard classification method and the non-spatially adaptive MRF_SPM adopting a fixed smoothing parameter, the spatially adaptive MRF_SPM with the smoothing parameter constrained to each pixel spectrum yielded sub-pixel maps not only with higher accuracy but also with shapes and boundaries visually reconstructed more closely to the reference map.     

Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.     

基于单亲遗传算法和子像元/像元空间吸引模型的亚像元制图研究

混合像元普遍存在于遥感图像数据中。与传统的硬分类(Hard Classification)方法相比,在处理混合像元时,软分类(Soft Classification)技术可以避免信息丢失;但是,通过软分类技术获得的结果,仍然无法确定各分类在像元中的具体位置。子像元制图(或超分辨率制图、亚像元制图)技术能将软分类技术得到的结果转化为更高分辨率的图像,它能兼得软分类和硬分类两者的优势。将遗传算法的一个变种-单亲遗传算法应用于子像元制图,结合子像元/像元空间吸引模型,单亲遗传算法能直接获得子像元制图结果。以合成的图像和实际的土地覆盖图像为实验对象,通过目视比较和定量精度评价,与硬分类的结果相比,该方法能取得更高的制图精度和更好的结果。     

Sub-pixel Mapping with Partheno-Genetic Algorithm and Sub-pixel/pixel Spatial Attraction Model

Mixed pixels are widely presented in remotely sensed images.Soft classification techniques can avoid the loss of information comparing to hard classification methods while handling mixed pixels.However,the assignment to these classes by soft classification does not specify the location in the pixel.Sub-pixel mapping (or super-resolution mapping) is a technique which designed to use the information obtained by soft classification to get a sharpened image and it can incorporate benefits of both hard and soft classification techniques.In this paper,a variation of genetic algorithm,named as partheno-genetic algorithm (PGA),is developed to accomplish the sub\|pixel mapping.With the sub-pixel/pixel attraction model,PGA can achieve sub-pixel mapping in a straightforward one-pass process.It is evaluated with artificial and degraded land cover images by visual and quantitative classification accuracy indices.The results show this method can increase accuracy while compared to hard classification.     

基于元胞自动机模型的遥感图像亚像元定位

由于遥感图像中普遍存在混合像元,因此传统分类方法得到的结果通常会存在较大误差,应用混合像元分解技术,虽然可以得到混合像元中各端元组分的丰度,但是却不能得到各端元组分的空间分布状态,而亚像元定位则是在混合像元分解的基础上,将混合像元剖分为亚像元,再利用端元组分的丰度及像元空间分布的特点,将亚像元赋予不同端元组分来得到各端元组分的空间分布情况,以提高遥感图像分类的精度。为了更好地解决亚像元定位问题,结合亚像元定位的理论模型,提出了一种新的元胞自动机模型,并通过模拟数据和实际数据对该模型进行了检验,结果表明,该模型是一种简单有效的解决亚像元定位问题的方法。     

Fraction images in multitemporal change detection

The concept of mixed pixels allows the interpretation of remote sensing digital image data at sub-pixel level. Fraction-image data, obtained using the notion of mixed pixels, offer a potentially powerful method to detect changes in land-cover over a given period of time. This study proposes a new approach to detect land-cover changes, using two sets of fraction-image data obtained from sets of multispectral image data acquired at two different dates, over the same area. Changes based on the selected pixel components are then used to generate the fraction-change image data, including both positive (increase) and negative (decrease) changes in each component. The proposed analysis is then performed in the fraction-change space in two different ways: (1) by implementing unsupervised classification methods and (2) by comparing the fraction-change images among themselves. The proposed methodology is tested on two sets of Landsat Thematic Mapper (TM) multispectral image data obtained at two different dates and covering a test area mapped in previous works. Results obtained by the proposed methodology are presented and discussed.     

Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery

Mixed pixels are widely existent in remote-sensing imagery. Although the proportion occupied by each class in mixed pixels can be determined by spectral unmixing, the spatial distribution of classes remains unknown. Sub-pixel mapping (SPM) addresses this problem and a sub-pixel/pixel spatial attraction model (SPSAM) has been introduced to realize SPM. However, this algorithm fails to adequately consider the correlation between sub-pixels. Consequently, the SPM results created by SPSAM are noisy and the accuracy is limited. In this article, a method based on particle swarm optimization is proposed as post-processing on the SPM results obtained with SPSAM. It searches the most likely spatial distribution of classes in each coarse pixel to improve the SPSAM. Experimental results show that the proposed method can provide higher accuracy and reduce the noise in the results created by SPSAM. When compared with the available modified pixel-swapping algorithm, the proposed method often yields higher accuracy results.     

基于目标优化的高光谱图像亚像元定位

目的 高光谱图像混合像元的普遍存在使得传统的分类技术难以准确确定地物空间分布,亚像元定位技术是解决该问题的有效手段。针对连通区域存在孤立点或孤立两点等特例时,通过链码长度求周长最小无法保证最优结果及优化过程计算量大的问题,提出了一种改进的高光谱图像亚像元定位方法。方法 以光谱解混结合二进制粒子群优化构建算法框架,根据光谱解混结果近似估计每个像元对应的亚像元组成,通过分析连通区域存在特例时基于链码长度求周长最小无法保证结果最优的原因,提出修改孤立区域的周长并考虑连通区域个数构造代价函数,最后利用二进制粒子群优化实现亚像元定位。为了减少算法的时间复杂度,根据地物空间分布特点,采用局部分析代替全局分析,提出了新的迭代优化策略。结果 相比直接基于链码长度求周长最小的优化结果,基于改进的目标函数优化后,大部分区域边界更明显,并且没有孤立1点和孤立两点的区域,识别率可以提高2%以上,Kappa系数增加0.05以上,新的优化策略可以使算法运算时间减少近一半。结论 实验结果表明,本文方法能有效提高亚像元定位精度,同时降低时间复杂度。因为高光谱图像中均匀混合区域不同地物的分布空间相关性不强,因此本文方法适用于非均匀混合的高光谱图像的亚像元定位。     

Locally adaptive linear mixture model-based super-resolution land-cover mapping based on a structure tensor

Super-resolution land-cover mapping (SRM) is a technique for generating land-cover thematic maps with a finer spatial resolution than the input image. Linear mixture model-based SRM (LSRM) is applied directly to a remotely sensed image and is composed of a spatial term that integrates the land-cover spatial pattern prior information, a spectral term that assumes that the spectral signature of each mixed pixel is composed of a weighted linear sum of endmember spectral signatures within that pixel and a balance parameter that defines the weight of the spatial term. The traditional LSRM adopts an isotropic spatial autocorrelation model in the land-cover spatial term for different classes and a fixed balance parameter for the entire image, and ignores the image local properties. The class boundaries are at risk of oversmoothing and may be imprecise, and the homogeneous regions may be unsmoothed and contain speckle-like artefacts in the result. This study proposes a locally adaptive LSRM (LA-LSRM) that integrates image local properties to predict fine spatial resolution pixel labels. The structure tensor is applied to detect the image local information. The LA-LSRM spatial term is locally adaptive and is composed of an anisotropic spatial autocorrelation model in which the spatial autocorrelation orientations of different classes may vary. The LA-LSRM balance parameter is locally adaptive to the different regions of the image. Such parameter obtains a relatively large value when the fine-resolution pixel is located in the homogeneous region to remove speckle-like artefacts and a relatively small value when the fine-resolution pixel is at the class boundary to preserve the edge. The LA-LSRM performance was assessed using a simulated multi-spectral image, an IKONOS multi-spectral image, a hyperspectral image produced by Airborne Visible/Infrared Imaging Spectrometer and a hyperspectral image produced by reflective optics system imaging spectrometer. Results show that the homogeneous regions were smoothed, the boundaries were better preserved and the overall accuracies were increased by LA-LSRM compared with traditional LSRM in all experiments.     

基于改进SAM的高光谱影像混合像元分解算法研究

将传统遥感图像分类方法中的光谱角度制图法(Spectral Angle Mapping-SAM)加以变换,改进为一种符合全约束条件下的高光谱遥感图像的混合像元分解模型.新算法在端元丰度比例满足全约束的条件下,通过逼近的方法寻找一种端元丰度的比例组合,使测试光谱与目标光谱的广义夹角最小,从而认为该比例组合就是混合像元分解...     

Soft-then-hard super-resolution mapping based on a spatial attraction model with multiscale sub-pixel shifted images

Super-resolution mapping (SRM) is a technique for exploring spatial distribution information of the land-cover classes at finer spatial resolution. The soft-then-hard super-resolution mapping (STHSRM) algorithm is a type of SRM algorithm that first estimates the soft class values for sub-pixels at the target fine spatial resolution and then predicts the hard class labels for sub-pixels. The sub-pixel shifted images from the same area can be incorporated to improve the accuracy of STHSRM algorithm. In this article, multiscale sub-pixel shifted images (MSSI) based on the fine-scale model and the coarse-scale model are utilized to increase the accuracy of STHSRM. First, class fraction images are derived from multiple sub-pixel shifted coarse spatial resolution images by soft classification. Then using the sub-pixel/sub-pixel spatial attraction model as fine-scale and the sub-pixel/pixel spatial attraction model as coarse scale, all MSSI can be derived from fraction images. The MSSI for each class are then integrated to obtain the desired fine spatial resolution images. Finally, the integrated fine spatial resolution images are used to allocate classes for sub-pixel. Experiments on two synthetic remote sensing images and a real hyperspectral remote sensing imagery show that the proposed method produces higher mapping accuracy result.     

Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil

The spatial and spectral variability of urban environments present fundamental challenges to deriving accurate remote sensing products for urban areas. Multiple endmember spectral mixture analysis (MESMA) is a technique that potentially addresses both challenges. MESMA models spectra as the linear sum of spectrally pure endmembers that vary on a per-pixel basis. Spatial variability is addressed by mapping sub-pixel components of land cover as a combination of endmembers. Spectral variability is addressed by allowing the number and type of endmembers to vary from pixel to pixel. This paper presents an application of MESMA to map the physical components of urban land cover for the city of Manaus, Brazil, using Landsat Enhanced Thematic Mapper (ETM+) imagery.We present a methodology to build a regionally specific spectral library of urban materials based on generalized categories of urban land-cover components: vegetation, impervious surfaces, soil, and water. Using this library, we applied MESMA to generate a total of 1137 two-, three-, and four-endmember models for each pixel; the model with the lowest root-mean-squared (RMS) error and lowest complexity was selected on a per-pixel basis. Almost 97% of the pixels within the image were modeled within the 2.5% RMS error constraint. The modeled fractions were used to generate continuous maps of the per-pixel abundance of each generalized land-cover component. We provide an example to demonstrate that land-cover components have the potential to characterize trajectories of physical landscape change as urban neighborhoods develop through time. Accuracy of land-cover fractions was assessed using high-resolution, geocoded images mosaicked from digital aerial videography. Modeled vegetation and impervious fractions corresponded well with the reference fractions. Modeled soil fractions did not correspond as closely with the reference fractions, in part due to limitations of the reference data. This work demonstrates the potential of moderate-resolution, multispectral imagery to map and monitor the evolution of the physical urban environment.     

Representation of an alpine treeline ecotone in SPOT 5 HRG data

An ecotone is a zone of vegetation transition between two communities, often resulting from a natural or anthropogenic environmental gradient. In remotely sensed imagery, an ecotone may appear as an edge, a boundary of mixed pixels or a zone of continuous variation, depending on the spatial scale of the vegetation communities and their transition zone in relation to the spatial resolution of the imagery. Often in image classification, an ecotone is either ignored if it falls within a width of one or two pixels, or part of it may be mapped as a separate vegetation community if it covers an area of several pixel widths. A soft classification method, such as probability mapping, is inherently appealing for mapping vegetation transition. Ideally, the probability of membership each pixel has to each vegetation class corresponds with the proportional composition of vegetation classes per pixel. In this paper we investigate the use of class probability mapping to produce a softened classification of an alpine treeline ecotone in Austria using a SPOT 5 HRG image. Here the transition with altitude is from dense subalpine forest to treeless alpine meadow and herbaceous vegetation. The posterior probabilities from a Maximum Likelihood algorithm are shown to reflect the land-cover composition of mixed pixels in the ecotone. The relationships between the posterior probability of class membership for the two end-member classes of ‘scrub and forest’ and ‘non-forest vegetation’ and the percentage ground cover of these vegetation classes (enumerated in 15 quadrats from 1:1500 aerial photographs) were highly significant: r² = 0.83 and r² = 0.85 respectively (p < 0.001, n = 15). We identify thresholds (alpha-cuts) in the posterior probabilities of class membership of ‘scrub and forest’ and ‘non-forest vegetation’ to map the alpine treeline ecotone as a transition zone of five intermediate vegetation classes between the end-member communities. In addition, we investigate the representation of the ecotone as a ratio between the posterior probabilities of ‘scrub and forest’ and ‘non-forest vegetation’. This displays the vegetation transition without imposing subjective boundaries, and has greater emphasis on the ecotone transition rather than on the end-member communities. We comment on the fitness for purpose of the different ways investigated for representing the alpine treeline ecotone.     

Sub-pixel land cover mapping for per-field classification

A method was developed to transform a soft land cover classification into hard land cover classes at the sub-pixel scale for subsequent per-field classification. First, image pixels were segmented using vector boundaries. Second, the pixel segments (ranked by area) were labelled with a land cover class (ranked by class typicality). Third, a hard per-field classification was generated by examining each polygon (representing a land cover parcel, or field) in its entirety (by grouping the fragments of the polygon contained within different image pixels) and assigning to it the modal land cover class. The accuracy of this technique was considerably higher than that of both a corresponding hard per-pixel classification and a perfield classification based on hard per-pixel classified imagery.